Training a RoBERTa-base language model on TPUs with TensorFlow and Hugging Face Transformers

AI Impact Summary

This guide demonstrates end-to-end training of a RoBERTa-base from scratch on TPUs using TensorFlow and Hugging Face Transformers, including tokenizer training, TFRecord data preparation, and GCS-based data streaming. It emphasizes XLA compatibility and TPUStrategy to distribute training across TPU pods, enabling significantly larger and faster runs than GPU-only workflows. For technical teams, the key implications are the need to prepare TFRecord shards, host data on GCS, and initialize models and tokenizers within a TPU strategy scope, with attention to memory and shard sizing. This approach unlocks scalable LM training, but requires TPU-capable infrastructure and careful data pipeline tuning.